• List of Articles


      • Open Access Article

        1 - Cost Benefit Analysis of Three Non-Identical Machine Model with Priority in Operation and Repair
        Nafeesa Bashir Raeesa Bashir JPS Joorel Tariq Rashid Jan Jan
        The paper proposes a new real life model and the main aim is to examine the cost benefit analysis of Textile Industry model subject to different failure and repair strategies. The reliability model comprises of three units i,e Spinning machine (S), Weaving machine (W), Full Text
        The paper proposes a new real life model and the main aim is to examine the cost benefit analysis of Textile Industry model subject to different failure and repair strategies. The reliability model comprises of three units i,e Spinning machine (S), Weaving machine (W), Colouring and Finishing machine(Cf). The working principal of the model starts with spinning machine (S) where in unit S is in operative state while as weaving machine, Colouring and Finishing machine are in ideal state. Complete failure of system is observed when all three units of system i.e. S,W and Cf are in down state. Repairperson is always available to carry out the repair activities in the system in which first priority in repair is given to Colouring and Finishing machine followed by Spinning and weaving machine. The proposed model attempts to maximize the reliability of a real life system. Reliability measures such as Mean Sojourn time, Mean time to system failure, Profit analysis of system are examined to define the performance of the reliability characteristics. For concluding the study of such model, different stochastic measures are analyzed in steady state using regenerative point technique. The tables are prepared for arbitrary values of the parameters to show the performance of some important reliability measures and to check the efficiency of the model under such situations. Manuscript Document
      • Open Access Article

        2 - DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation
        Adeep  Biswas Debayan  Bhattacharya Anil Kumar Kakelli
        The videos are more popular for sharing content on social media to capture the audience’s attention. The artificial manipulation of videos is growing rapidly to make the videos flashy and interesting but they can easily misuse to spread false information on social media Full Text
        The videos are more popular for sharing content on social media to capture the audience’s attention. The artificial manipulation of videos is growing rapidly to make the videos flashy and interesting but they can easily misuse to spread false information on social media platforms. Deep Fake is a problematic method for the manipulation of videos in which artificial components are added to the video using emerging deep learning techniques. Due to the increase in the accuracy of deep fake generation methods, artificially created videos are no longer detectable and pose a major threat to social media users. To address this growing problem, we have proposed a new method for detecting deep fake videos using 3D Inflated Xception Net with Discrete Fourier Transformation. Xception Net was originally designed for application on 2D images only. The proposed method is the first attempt to use a 3D Xception Net for categorizing video-based data. The advantage of the proposed method is, it works on the whole video rather than the subset of frames while categorizing. Our proposed model was tested on the popular dataset Celeb-DF and achieved better accuracy. Manuscript Document
      • Open Access Article

        3 - An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier
        Ramesh G Prasanna  G B Santosh  V Bhat Chandrashekar  Naik Champa  H N
        Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision. The event of the handwritten digit is generous. With a wide opportunity, the issue of handwritten digit recognition by using computer vision and m Full Text
        Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision. The event of the handwritten digit is generous. With a wide opportunity, the issue of handwritten digit recognition by using computer vision and machine learning techniques has been a well-considered upon field. The field has gone through an exceptional turn of events, since the development of machine learning techniques. Utilizing the strategy for Support Vector Machine (SVM) and Principal Component Analysis (PCA), a robust and swift method to solve the problem of handwritten digit recognition, for the Kannada language is introduced. In this work, the Kannada-MNIST dataset is used for digit recognition to evaluate the performance of SVM and PCA. Efforts were made previously to recognize handwritten digits of different languages with this approach. However, due to the lack of a standard MNIST dataset for Kannada numerals, Kannada Handwritten digit recognition was left behind. With the introduction of the MNIST dataset for Kannada digits, we budge towards solving the problem statement and show how applying PCA for dimensionality reduction before using the SVM classifier increases the accuracy on the RBF kernel. 60,000 images are used for training and 10,000 images for testing the model and an accuracy of 99.02% on validation data and 95.44% on test data is achieved. Performance measures like Precision, Recall, and F1-score have been evaluated on the method used. Manuscript Document
      • Open Access Article

        4 - Overcoming the Link Prediction Limitation in Sparse Networks using Community Detection
        Mohammad Pouya Salvati Jamshid  Bagherzadeh Mohasefi Sadegh Sulaimany
        Link prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A maj Full Text
        Link prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A major issue confronted by all these methods, is that many of the available networks are sparse. This results in high volume of computation, longer processing times, more memory requirements, and more poor results. This research has presented a new, distinct method for link prediction based on community detection in large-scale sparse networks. Here, the communities over the network are first identified, and the link prediction operations are then performed within each obtained community using neighbourhood-based methods. Next, a new method for link prediction has been carried out between the clusters with a specified manner for maximal utilization of the network capacity. Utilized community detection algorithms are Best partition, Link community, Info map and Girvan-Newman, and the datasets used in experiments are Email, HEP, REL, Wikivote, Word and PPI. For evaluation of the proposed method, three measures have been used: precision, computation time and AUC. The results obtained over different datasets demonstrate that extra calculations have been prevented, and precision has been increased. In this method, runtime has also been reduced considerably. Moreover, in many cases Best partition community detection method has good results compared to other community detection algorithms. Manuscript Document
      • Open Access Article

        5 - Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks
        Elham Gholam Seyed Reza Kamel Tabbakh maryam khairabadi
        Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting nu Full Text
        Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting numerous tests and imagings, which are costly and time-consuming. Therefore, doctors are seeking a cost-effective and time-efficient alternative. One of the medical solutions is Chinese medicine and diagnosis by observing changes of the tongue. Detecting the disease using tongue appearance and color of various sections of the tongue is one of the key components of traditional Chinese medicine. In this study, a method is presented which can carry out the localization of tongue surface regardless of the different poses of people in images. In fact, if the localization of face components, especially the mouth, is done correctly, the components leading to the biggest distinction in the dataset can be used which is favorable in terms of time and space complexity. Also, since we have the best estimation, the best features can be extracted relative to those components and the best possible accuracy can be achieved in this situation. The extraction of appropriate features in this study is done using deep convolutional neural networks. Finally, we use the random forest algorithm to train the proposed model and evaluate the criteria. Experimental results show that the average classification accuracy has reached approximately 73.78 which demonstrates the superiority of the proposed method compared to other methods. Manuscript Document
      • Open Access Article

        6 - Cluster-based Coverage Scheme for Wireless Sensor Networks using Learning Automata
        Ali Ghaffari Seyyed Keyvan  Mousavi
        Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the d Full Text
        Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the density of nodes become high in some areas and low in some other. In this case, some areas are not covered by none of sensor nodes which these areas are called coverage holes. Also, creating areas with high density leads to redundant overlapping and as a result the network lifetime decreases. In this paper, a cluster-based scheme for the coverage problem of WSNs using learning automata is proposed. In the proposed scheme, each node creates the action and probability vectors of learning automata for itself and its neighbors, then determines the status of itself and all its neighbors and finally sends them to the cluster head (CH). Afterward, each CH starts to reward or penalize the vectors and sends the results to the sender for updating purposes. Thereafter, among the sent vectors, the CH node selects the best action vector and broadcasts it in the form of a message inside the cluster. Finally, each member changes its status in accordance with the vector included in the received message from the corresponding CH and the active sensor nodes perform environment monitoring operations. The simulation results show that the proposed scheme improves the network coverage and the energy consumption. Manuscript Document
      • Open Access Article

        7 - Energy Efficient Cross Layer MAC Protocol for Wireless Sensor Networks in Remote Area Monitoring Applications
        R Rathna L Mary Gladence J Sybi Cynthia V Maria Anu
        Sensor nodes are typically less mobile, much limited in capabilities, and more densely deployed than the traditional wired networks as well as mobile ad-hoc networks. General Wireless Sensor Networks (WSNs) are designed with electro-mechanical sensors through wireless d Full Text
        Sensor nodes are typically less mobile, much limited in capabilities, and more densely deployed than the traditional wired networks as well as mobile ad-hoc networks. General Wireless Sensor Networks (WSNs) are designed with electro-mechanical sensors through wireless data communication. Nowadays the WSN has become ubiquitous. WSN is used in combination with Internet of Things and in many Big Data applications, it is used in the lower layer for data collection. It is deployed in combination with several high end networks. All the higher layer networks and application layer services depend on the low level WSN in the deployment site. So to achieve energy efficiency in the overall network some simplification strategies have to be carried out not only in the Medium Access Control (MAC) layer but also in the network and transport layers. An energy efficient algorithm for scheduling and clustering is proposed and described in detail. The proposed methodology clusters the nodes using a traditional yet simplified approach of hierarchically sorting the sensor nodes. Few important works on cross layer protocols for WSNs are reviewed and an attempt to modify their pattern has also been presented in this paper with results. Comparison with few prominent protocols in this domain has also been made. As a result of the comparison one would get a basic idea of using which type of scheduling algorithm for which type of monitoring applications. Manuscript Document